Production Data Rationalisation

A large global oil company was facing issues with inconsistent and duplicated data, leading to confusion with local government on production reporting. Technical staff were working with different data sets and the central data store was untrusted as it was known to store incomplete production history.

Venture was commissioned to create a rationalised set of historical production data to allow users to work with a single complete data set. Our understanding of production data, our expertise in data management and our use of Ventures unique in-house tool kit, V-KIT, were attributes valued by the client.

Background

A single definitive production history was lacking; several different data sets existed, each focussed on different fields or reservoirs. Data was inconsistent between datasets and held at different levels of quality and completeness. In addition, as users did not know what data to trust, they were building and maintaining their own private copies.

Assessing various data sources for data quality using a unique analysis process developed by Venture.

- This process analysed the data and assigned a quality score, based on matching data points in each of the available datasets

-The analysis included the creation of comparative charts to pinpoint where data points diverged between the different datasets.

Working with the clients technical experts to understand the data sources and their provenance.

Creating a full end to end automated process and deployment application to select and test the data rationalisation, enabling it to be progressively loaded to development, test and production data stores. This process was designed to load the data from various sources as agreed by users.

Benefits

Accurate cumulative production reporting achieved by adding historical figures for missing time periods, wells and reservoirs.

Ready access to the data - the new data set was prepared to coincide with the launch of a new user application to access the database.

Increased governmental trust - a single trusted data set reduces data muddle, decreases confusion in production reporting and hence helps to build overall confidence and trust.